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<h2 class="title">
<a id="_indexing_employee_documents"></a>Indexing Employee Documents<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/010_Intro/25_Tutorial_Indexing.asciidoc">edit</a>
</h2>
</div></div></div>
<p>The first order of business is storing employee data.  This will take the form
of an <em>employee document</em>: a single document represents a single
employee.  The act of storing data in Elasticsearch is called <em>indexing</em>, but
before we can index a document, we need to decide <em>where</em> to store it.</p>
<p>An Elasticsearch cluster can contain multiple <em>indices</em>, which in
turn contain multiple <em>types</em>. These types hold multiple <em>documents</em>,
and each document has multiple <em>fields</em>.</p>
<div class="sidebar">
<div class="titlepage"><div><div>
<p class="title"><strong>Index Versus Index Versus Index</strong></p>
</div></div></div>
<p>You may already have noticed that the word <em>index</em> is overloaded with
several meanings in the context of Elasticsearch. A little
clarification is necessary:</p>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
Index (noun)
</span>
</dt>
<dd>
As explained previously, an <em>index</em> is like a <em>database</em> in a traditional
relational database. It is the place to store related documents. The plural of
<em>index</em> is <em>indices</em> or <em>indexes</em>.
</dd>
<dt>
<span class="term">
Index (verb)
</span>
</dt>
<dd>
<em>To index a document</em> is to store a document in an <em>index (noun)</em> so
that it can be retrieved and queried. It is much like the <code class="literal">INSERT</code> keyword in
SQL except that, if the document already exists, the new document would
replace the old.
</dd>
<dt>
<span class="term">
Inverted index
</span>
</dt>
<dd>
<p>
Relational databases add an <em>index</em>, such as a B-tree index, to specific
columns in order to improve the speed of data retrieval.  Elasticsearch and
Lucene use a structure called an <em>inverted index</em> for exactly the same
purpose.
</p>
<p>By default, every field in a document is <em>indexed</em> (has an inverted index)
and thus is searchable. A field without an inverted index is not searchable.
We discuss inverted indexes in more detail in <a class="xref" href="inverted-index.html" title="Inverted Index">Inverted Index</a>.</p>
</dd>
</dl>
</div>
</div>
<p>So for our employee directory, we are going to do the following:</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
Index a <em>document</em> per employee, which contains all the details of a single
employee.
</li>
<li class="listitem">
Each document will be of <em>type</em> <code class="literal">employee</code>.
</li>
<li class="listitem">
That type will live in the <code class="literal">megacorp</code> <em>index</em>.
</li>
<li class="listitem">
That index will reside within our Elasticsearch cluster.
</li>
</ul>
</div>
<p>In practice, this is easy (even though it looks like a lot of steps).  We
can perform all of those actions in a single command:</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">PUT /megacorp/employee/1
{
    "first_name" : "John",
    "last_name" :  "Smith",
    "age" :        25,
    "about" :      "I love to go rock climbing",
    "interests": [ "sports", "music" ]
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/010_Intro/25_Index.json"></div>
<p>Notice that the path <code class="literal">/megacorp/employee/1</code> contains three pieces of
information:</p>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">megacorp</code>
</span>
</dt>
<dd>
The index name
</dd>
<dt>
<span class="term">
<code class="literal">employee</code>
</span>
</dt>
<dd>
The type name
</dd>
<dt>
<span class="term">
<code class="literal">1</code>
</span>
</dt>
<dd>
The ID of this particular employee
</dd>
</dl>
</div>
<p>The request body—​the JSON document—​contains all the information about
this employee.  His name is John Smith, he’s 25, and enjoys rock climbing.</p>
<p>Simple!  There was no need to perform any administrative tasks first, like
creating an index or specifying the type of data that each field contains. We
could just index a document directly.  Elasticsearch ships with defaults for
everything, so all the necessary administration tasks were taken care of in
the background, using default values.</p>
<p>Before moving on, let’s add a few more employees to the directory:</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">PUT /megacorp/employee/2
{
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}

PUT /megacorp/employee/3
{
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/010_Intro/25_Index.json"></div>
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